论文标题
通过细粒知识转移的图像翻译
Image Translation via Fine-grained Knowledge Transfer
论文作者
论文摘要
盛行的图像翻译框架主要寻求通过端到端样式处理图像，这取得了令人信服的结果。尽管如此，这些方法缺乏可解释性，并且在不同的图像翻译任务（例如样式传输，HDR等）上不可扩展。在本文中，我们提出了一个基于知识的图像翻译框架，该框架通过知识检索和转移实现了图像翻译。从详细的范围内，该框架构建了插件和模型的通用知识库，记住特定于任务的样式，音调，纹理模式等。此外，我们提出了一种快速的ANN搜索方法，Bandpass层级级别K-Means（BHKM），以应对在巨大的知识库中难以搜索的搜索。广泛的实验很好地证明了我们在不同图像翻译任务中框架的有效性和可行性。特别是，回溯实验验证了我们方法的解释性。我们的代码很快将在https://github.com/acesix/knowledge_transfer上找到。
Prevailing image-translation frameworks mostly seek to process images via the end-to-end style, which has achieved convincing results. Nonetheless, these methods lack interpretability and are not scalable on different image-translation tasks (e.g., style transfer, HDR, etc.). In this paper, we propose an interpretable knowledge-based image-translation framework, which realizes the image-translation through knowledge retrieval and transfer. In details, the framework constructs a plug-and-play and model-agnostic general purpose knowledge library, remembering task-specific styles, tones, texture patterns, etc. Furthermore, we present a fast ANN searching approach, Bandpass Hierarchical K-Means (BHKM), to cope with the difficulty of searching in the enormous knowledge library. Extensive experiments well demonstrate the effectiveness and feasibility of our framework in different image-translation tasks. In particular, backtracking experiments verify the interpretability of our method. Our code soon will be available at https://github.com/AceSix/Knowledge_Transfer.
